Day 1: Introduction to AI and ISO 42001 Overview

• Introduction to Artificial Intelligence (AI) 
• What is AI?
• Key AI technologies and their applications
• Challenges and risks associated with AI
• The need for AI governance and ethics
• Overview of ISO 42001 
• Introduction to the ISO 42001 standard
• Structure of ISO 42001
• Key concepts and principles
• Benefits of implementing an AI Management System (AIMS)
• The relationship between ISO 42001 and other management systems (e.g., ISO 9001, ISO 27001)

• Understanding the AI Governance Framework 
• Ethical AI and AI governance principles
• AI lifecycle and decision-making processes
• Key stakeholders in AI management (e.g., AI developers, users, regulators)
• ISO 42001 Key Clauses: 
• Clause 1- Scope
• Clause 2 - Normative References
• Clause 3 - Terms and Definitions
• Clause 4 - Context of the Organization

Day 2: AI Risk Management and Requirements for an AIMS

• Risk Management in AI Systems
• Identifying AI-related risks (e.g., bias, security, transparency)
• Risk assessment techniques in AI
• AI risk treatment and mitigation strategies
• Clause 5 - Leadership and Commitment
• Leadership’s role in AI governance
• Establishing an AI governance committee
• AI strategy alignment with organizational goals
• AI System Design and Development Requirements 
• Clause 6 - Planning and Risk Assessment for AI Systems
• Designing and developing AI systems in accordance with ethical principles
• Ensuring fairness, accountability, and transparency
• Clause 7 - Support 
• Resources, competencies, and training
• Internal and external communication
• Documentation and record-keeping
• AI Lifecycle Management 
• The importance of ongoing monitoring and auditing
• Managing AI performance post-deployment
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Day 3: Implementing and Monitoring AI Management Systems

• Clause 8 - Operations and Control of AI Systems 
• The operationalization of AI management systems
• Control mechanisms for monitoring AI systems
• Ensuring data privacy and security
• AI Implementation Strategies 
• Step-by-step approach to implementing AIMS
• Integrating AI systems into business operations
• Setting measurable AI performance objectives
• Clause 9 - Performance Evaluation 
• Monitoring, measuring, and evaluating AI system performance
• AI audits, internal reviews, and continuous improvement
• Reporting on AI performance and system issues
• Clause 10 - Improvement 
• Identifying opportunities for AI system improvements
• Corrective actions for addressing non-conformities
• Continual improvement practices for AI governance

Day 4: Audit, Compliance, and Certification

• AI Compliance and Legal Requirements
• Legal and regulatory considerations in AI (e.g., GDPR, AI Act, data protection)
• Ensuring compliance with international AI guidelines
• Ethical AI: ensuring fairness and accountability in AI systems
• Preparing for an ISO 42001 Audit
• Audit process and requirements for AIMS
• Key audit criteria and expectations
• Preparing documentation for ISO 42001 certification
• Role of the Lead Implementer during audits
• Practical Workshop 
• Real-life case studies on implementing ISO 42001 in different organizations
• Developing a sample AI Management System implementation plan
• Group exercise: Building an AIMS implementation roadmap
• Final Exam and Q&A Session 
• Recap of key concepts
• Q&A session to clarify doubts
• Final examination on Certified ISO 42001 Lead Implementer